Optimisation, Simulation and Decision Support Systems
The efficient solution of a planning, management, or operational control problem can be obtained by appropriate optimisation algorithms, which, in turn, rely on simulation models to explore the space of alternative solutions, but also to assess the potential impact of the proposed solution in a simulated realistic environment. The generation and the evaluation of such solutions is suitably organised and orchestrated by a decision support system.

Optimization is a vast research area which encompasses many disciplines, from mathematics to operations research, from systems theory to control engineering. Any man-made system can be optimised in its function and  behaviour and thus the applications of optimisation are practically unlimited. At IDSIA we focus our research on the optimisation of combinatorially-complex systems by developing innovative methods such as metaheuristics for optimisation (e.g. the ant-colony optimisation metaheuristic), robust algorithms, and approximation algorithms.

Simulation allows to make experiments in "virtual world" where the impossible becomes possible. Computer-based simulation allows to recreate on a computer chip systems configurations and experimental settings that would be either impractical or impossible to recreate in the real world. IDSIA has an expertise in discrete-event simulation in the context of logistics systems, where detailed simulation models can be used to evaluate the performance of alternative optimised planning and management policies.

Decision Support Systems (DSSs) help decision makers to analyse, understand and explore complex decisional processes and therefore make informed decisions to solve real-world problems. Decision Support Systems integrate optimization, modeling and simulation in a computer-based environment, in order to provide the required level of insight in the decisional problem.  The development of an effective real-world DSS therefore requires a formalization of the decisional problem, the definition of the preference criteria of the decision makers and possibly of all stakeholders, a simulation of the possible scenarios in order to evaluate their performance, and an exact or heuristic optimization methodology to screen alternatives and focus only on efficient ones. The DSS must be able to access large data sets to support its analyses and provide an intuitive and effective way to display the results and interact with the decision makers.

At IDSIA, a Computational Biophysics Unit is also active. CBU employs a wide spectrum of molecular and multiscale computational techniques to study complex biological systems. Activities include in particular: drug delivery system and nanoparticle design and optimization, in silico structure- and ligand-based virtual screening with investigation of drug mechanism of action and self-assembly of biopolymers.


  • Energy and Environment
  • Business Engineering and Production
  • Industrial Technologies
  • Information and Communication Technologies

We have studied a wide range of problems (e.g. traveling salesman, quadratic assignment, vehicle routing, job shop scheduling, sequential ordering, network routing) and a number of different instances including dynamic, probabilistic, stochastic, online, distributed, and real-world applications. In several cases of interest we have been able to compute the best-known solutions for reference benchmark instances. To solve these problems IDSIA has successfully developed a number of heuristics and exact algorithms such as the ant colony optimization algorithms, inspired by the behavior of real ant colonies. Mathematical programming algorithms have been applied to vehicle routing problems in which the uncertainty is expressed in the  form of intervals, with the aim of finding robust and sound solutions.